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Let AI agents query video as a local media asset.
Copy the install command and let the AI configure it · recommended for beginners
Please install the "io.github.thatsrajan/vidlens-mcp" MCP server from askskill: Run: claude mcp add 'io-github-thatsrajan-vidlens-mcp' -- npx -y vidlens-mcp
Use the Vidlens MCP to treat this local video as a queryable asset and find the segments that mention the product launch date, listed in chronological order.
Returns relevant video segments or timestamps in chronological order.
Use the Vidlens MCP to analyze this YouTube video, identify parts related to pricing strategy, and summarize the key points.
Provides pricing-related segment locations and a concise summary.
Use the Vidlens MCP to search this set of social media videos and find all content that mentions the brand name for later reuse.
Summarizes where the brand name appears across videos for easier review and reuse.
Developers or researchers can give local videos to AI agents as queryable assets to quickly locate specific topics, segments, or clues. This is more efficient than manually watching videos.
Content teams can use this tool to search YouTube and social media videos and quickly find content related to a topic or brand. It is useful for asset review and content organization.
When a team wants AI to work with video as a local media asset, this MCP tool can serve as the query layer. It fits workflows that connect agents with media retrieval tasks.
It is an MCP tool that lets AI agents use video as a queryable local asset. The description says it offers 47 tools for YouTube, social, and local media.
Based on the given description, it works with YouTube, social media, and local media. For the exact supported scope, see the source repository.
The provided material does not include installation, dependency, or configuration steps. See the source repository for integration details.
Generate images and videos automatically, then stitch clips locally into final media.
Generate and analyze videos and images, and download Douyin and Xiaohongshu videos.
Analyze videos with frame extraction, scene detection, and metadata retrieval.
Turn videos into frames and transcripts for deeper AI analysis
Let AI agents perform headless video editing, rendering, QC, and search.
Create video compositions and VFX programmatically through MCP for AI-directed production.